Software Developer - Java, Backend & AI Engineering

Software Developer - Java, Backend & AI Engineering

📍 McKinney, Texas

About BeeHyv

BeeHyv is a boutique software engineering firm helping product companies, mid-market enterprises, and PE-backed firms modernise systems, build cloud-native platforms, and adopt AI and GenAI solutions.

We combine the agility of a product engineering team with the technical depth required to solve complex enterprise challenges. Our engineers work closely with clients to build scalable platforms, modernise existing applications, and introduce emerging technologies into production environments.

The Role

We are looking for a Software Developer with strong Java and backend engineering fundamentals who is interested in building enterprise-grade AI-enabled applications and autonomous agent-based systems.

In this role, you will contribute to the design and development of intelligent software agents that automate business processes, interact with enterprise systems, process data, make decisions, invoke tools, and coordinate multi-step workflows.

You will work across traditional backend engineering and emerging AI engineering. This includes building reliable Java services, integrating large language models, designing agent workflows, managing application state, connecting with databases and cloud services, and ensuring that AI-driven functionality is secure, observable, testable, and production-ready.

This is a hands-on engineering role suited for someone who enjoys understanding complex systems, solving backend problems, experimenting with emerging technologies, and turning early ideas into maintainable production solutions.

What You’ll Do

• Design, develop, test, deploy, and maintain backend applications using Java and Spring Boot
• Build enterprise AI agents that perform specialised tasks and automate multi-step business workflows
• Design agent orchestration involving routing, decision-making, retries, tool execution, memory, and state management
• Develop reusable components and frameworks that support multiple AI agents and use cases
• Integrate large language models with backend applications and enterprise services
• Work with Java-based AI frameworks such as LangChain4j, LangGraph4j, Spring AI, or similar technologies
• Design prompts, tools, structured outputs, validation rules, guardrails, and fallback mechanisms
• Build REST APIs, asynchronous workflows, and streaming interfaces for AI-enabled applications
• Connect AI agents with databases, internal APIs, cloud services, search systems, and analytical platforms
• Develop agent tools that retrieve data, execute approved operations, and interact with enterprise systems
• Implement conversation memory, workflow state, checkpointing, and persistence where required
• Work with relational databases and design efficient data models and queries
• Use caching and distributed state-management platforms such as Redis or Valkey
• Add tracing, metrics, logs, and observability across backend services, LLM calls, tools, and agent workflows
• Evaluate agent behaviour, model responses, latency, token usage, reliability, and output quality
• Write unit, integration, and end-to-end tests for backend and AI-enabled functionality
• Diagnose issues across application code, databases, caches, cloud services, APIs, and model integrations
• Participate in code reviews, architecture discussions, debugging, and performance optimisation
• Work with Git-based workflows, Jira, and CI/CD pipelines
• Use Docker and containerised services for development and deployment
• Collaborate with senior engineers, architects, product teams, and client stakeholders
• Understand business requirements and translate them into reliable technical solutions
• Contribute to documentation, engineering standards, automation, and reusable development patterns

What We’re Looking For

• Undergraduate or postgraduate degree in Computer Science, Engineering, Information Technology, or a related technical field
• Two or more years of experience developing backend systems, APIs, data applications, or enterprise software
• Strong understanding of programming fundamentals, object-oriented programming, data structures, and problem-solving
• Hands-on experience with Java
• Experience building backend applications using Spring Boot
• Understanding of REST APIs, dependency injection, application configuration, exception handling, and layered architecture
• Strong knowledge of SQL, joins, relational databases, and basic query optimisation
• Experience with PostgreSQL, MySQL, or another relational database
• Understanding of Git workflows, branching, pull requests, and code reviews
• Familiarity with Maven or Gradle
• Experience debugging applications using logs, IDE tools, and runtime traces
• Ability to understand and safely extend an existing enterprise codebase
• Ability to design clean, reusable, and maintainable software components
• Strong written and verbal communication skills
• Ability to work independently and collaborate effectively within a team
• Curiosity, adaptability, and a strong interest in AI and emerging technologies

Preferred Qualifications

• Experience with LangChain4j, LangGraph4j, Spring AI, or another AI application framework
• Understanding of AI agents, tool calling, workflow orchestration, prompt engineering, or structured model outputs
• Experience designing workflows involving routing, planning, retries, approvals, and human-in-the-loop interactions
• Understanding of retrieval-augmented generation, embeddings, vector search, or enterprise search
• Experience building tools or connectors that allow agents to interact with APIs, databases, or external systems
• Familiarity with Redis, Valkey, or another distributed caching platform
• Experience with Micrometer, OpenTelemetry, Langfuse, Grafana, Prometheus, or similar observability platforms
• Knowledge of distributed tracing concepts such as traces, spans, context propagation, and correlation IDs
• Familiarity with monitoring LLM calls, agent steps, tool executions, token usage, and model latency
• Experience with Docker, Docker Compose, or container-based development
• Experience with AWS, Azure, or GCP
• Exposure to CI/CD tools such as Jenkins, GitLab CI, or GitHub Actions
• Understanding of secure application development, authentication, authorisation, secrets management, and environment-specific configuration
• Experience with JSON Schema, structured response models, validation frameworks, or typed model outputs
• Familiarity with JUnit, Mockito, Spring Boot Test, or similar testing tools
• Exposure to Python for scripting, automation, or data processing
• Experience using AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, or similar tools
• Familiarity with payments, financial technology, banking, enterprise analytics, or workflow automation is a plus

Technologies You May Work With

• Java and Spring Boot
• LangChain4j, LangGraph4j, and Spring AI
• Large language models and agent-based architectures
• AWS Bedrock and other managed AI platforms
• PostgreSQL
• Redis or Valkey
• REST APIs, asynchronous processing, and Server-Sent Events
• Micrometer, OpenTelemetry, and Langfuse
• Maven
• Docker and Docker Compose
• Git, GitLab, Jira, and Jenkins
• AWS, Azure, or GCP
• Linux-based development environments

Why Join Us

• Build multiple enterprise AI agents and intelligent workflow applications
• Work on practical GenAI use cases beyond basic chatbots and proof-of-concept applications
• Learn how AI models, backend services, databases, APIs, tools, and cloud systems work together
• Gain hands-on experience designing reliable and production-ready agent architectures
• Build strong Java, Spring Boot, cloud, data, and distributed-systems fundamentals
• Work on both modernisation initiatives and new AI-enabled applications
• Contribute to reusable platforms and components that support future AI agents
• Collaborate closely with senior engineers, architects, and client stakeholders
• Learn production engineering practices for reliability, security, observability, testing, and deployment
• Be part of an engineering-led culture that values clean code, experimentation, ownership, and continuous learning
• Take on increasing technical responsibility as your understanding of the systems and business domains grows